Big Data Visualization
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Transcript of Big Data Visualization
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Kwan-Liu Ma Department of Computer Science University of California at Davis
Big Data Visualization
CA Technologies 1/22/2014
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Big Data: Issues • Volume: size/scale • Velocity: rate • Variety: type/form • Veracity: accuracy and completeness
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Visualiza0on • To explore and discover • To validate • To communicate
• An overview, a path, an interface
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Extreme-‐Scale Scien0fic Simula0ons
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Scien>fic Simula>ons
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Large Scien>fic Data Visualiza>on
• In situ visualiza>on • Parallel visualiza>on that is highly scalable • In situ data reduc>on and triage • In situ data processing for interac>ve data explora>on and analysis
As we move to Exascale, it’s no longer feasible to store most of the data for post processing! We must do:
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Supernova Simula>on
Simulation: John Blondin, NCSU
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Fusion Simula>ons
Simulation: Dr. S. Ethier, the Princeton Plasma Physics Lab.
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Big Network Analysis & Visualiza0on
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FM3
GRIP
Treemap
Hilbert
Sunburst
Circle
222 nodes 2583 edges
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Network Simplifica>on/Characteriza>on
Hamas
al Qaeda
TVCG 12(6) 2006
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Network Simplifica>on/Characteriza>on
Friendster social network Astrophysics co-author network Links exhibit negative sensitivity (red) One competitive network (red) and between cluster centers one collaborative network (blue)
Using centrality sensitivity
Competitive
Collaborative
TVCG 18(1) 2012
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The Graph Layout Problem • The cost of displaying a graph
• The hairball problem of large graph layouts – Large, dense graphs become
a mess – Inefficient use of space – Details cluLered
• Solu>ons – Filtering – Clustering – Abstrac>on – Focus+context California data 6,107 nodes 15,160 edges
High dimensional embedding method
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A Fast Graph Layout Method l Hierarchically cluster the nodes (if no clustering given) l Traverse the hierarchy to order the nodes l Place the nodes in that order along a space filling curve
Order 1 Order 2 Order 3 Order 4 Order 5 Order 11
Hilbert curves
TVCG 14(6) 2008
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Fast Graph Layout A Graph with 6,107 nodes 15,160 edges
Hibert Space filling curve: Gosper
Treemap
High dimensional embedding: 0.19s
One time clustering: 0.5 seconds Layout + rendering: 0.0005 seconds
LinLog (force directed): 10,737s
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Fast Graph Layout Internet Connectivity 41,928 nodes 218,080 edges
Space filling curve: Hibert
Space filling curve: Gosper FM3 40.8s
GRIP 6.87s
One time clustering: 18.87 seconds Layout + rendering: 0.0036 seconds
Treemap
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Dynamic Networks
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Growing Internet Incremental clustering-based approach – Radial treemap layout
Video
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Time-‐Varying Networks
• Almost all networks found in real-‐world applica>ons are >me-‐varying
• Both nodes and edges can change • Visualiza>on methods:
– Anima>ons – Small mul>ples visualiza>on – Difference visualiza>on – Storyline visualiza>on
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Storyline Visualiza>on
XKCD.com
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Storyline Visualiza>on
• Consis>ng of a series of lines, going from leU to right along the >me-‐axis, that converge and diverge in the course of their paths.
• Each line represents a unique en>ty (character) in the data.
• The star>ng & ending points of each line represent the lifespan of the corresponding en>ty.
• Lines are bundled together during the >me period of their interac>on.
• Exis>ng algorithms: 1. Rules and heuris>cs based [Ogawa & Ma 2008] 2. Gene>c algorithm [Tanahashi & Ma 2012] 3. Convex quadra>c op>miza>on [Liu et al. 2013] 4. Greedy algorithms
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Star Wars
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Matrix
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Incep0on
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Star Wars
Video
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Enron Scandal Email Data 1230 days, 1264 employees, 495,408 messages, and 3478 email clusters
Video
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Current Projects • Dynamic network visualiza>on [Biological science, Internet, social networks] • Visual recommenda>ons and predic>ve analysis [Transporta>on] • Visual analy>cs for cyber and airborne intelligence • Remote and collabora>ve visualiza>on • Volume data visualiza>on [Flow simula>on, biomedical imaging, NDT] • Health record visualiza>on • Visual analysis of driving behaviors and energy use [Transporta>on] • Visualiza>on for scien>fic storytelling • Massively parallel visualiza>on • In situ visualiza>on and data reduc>on • Visualizing large scale compu>ng [Scien>fic compu>ng, cloud compu>ng] • Video visualiza>on [Security] • Uncertainty visualiza>on • Visualiza>on interface design